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# System prepended metadata

title: Block assignment 1

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# Block assignment 1

<iframe width="560" height="315" src="https://www.youtube.com/embed/iuABouMOO-w" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

*Video demonstration of how our prototype works*

#### Introduction

In the process of creating a computational prototype, our focus has been on Data visualisation including both the hidden and visible parts thereof. We encounter data visualisation in many aspects of our daily lives, nonetheless they have become such a valuable tool, that we rarely stop to reflect upon how these visualisations are made, or which data they represent. A data visualisation often substitutes an important argument, with its convincing rhetoric building upon numbers. 

We set out to investigate what role data plays in the process of making a data visualisation. How choices about data sources, data cleaning, transformation and many other factors are a part of visualising data. 

#### Method

For many years, even millennials, there has been a cultural practice of collecting, storing and analysing data. The volume, variety and use of data has grown, and the ways in which data can be used has grown enormously. Kitchen and Lauriault argues in “Towards critical data studies: Charting and unpacking data assemblages and their work” (2014); that data was formerly understood as pre-analytical and pre-factual, existing prior to interpretation and argument. Following this understanding, data was a raw material on which knowledge was built, a matter that was somewhat objective and representative of the knowledge it represented, in this way it is not the data itself, but the uses of data that are political. With the rise of Critical Data Studies, this view has been challenged; “ [...] data are constitutive of the ideas, techniques, technologies, people, systems and contexts that conceive, produce, process, manage and analyse them.” (Kitchen & Lauriault 2014, 5). With this understanding follows, that data is newer a raw objective material, it is before collection, during data cleaning, and with data visualisation processed and transformed. Drawing upon the critical data studies approach we set out to explore the different layers of data visualisation.


#### Prototype 

The initial idea was to explore data visualisation, by creating our own tool to visualise irregularities in datasets. By drawing inspiration from the Herman Chernoff face approach; to visualise multivariate data in human faces (Chernoff, 1973), we wanted to create a prototype from which we could show inequality in different europeans countries through visualisations of faces. 

![](https://i.imgur.com/yMSJ1Fw.png)


With this as our main goal, we had a framework in which we could investigate the different layers of data visualisation, as follows: 

1. **Data:** Finding and investigating already existing datasets on inequality → analysing potential biases and choices of visualisation of the chosen datasets → cleaning chosen data, and hereby transforming it into our own dataset.
    
    **Outcome:** Reflecting upon the infrastructure of the already existing datasets, their biases, but also our own choices of how to transform the data into something usable for the project. 
2. **Visualisation/Building:** Choosing materials → conforming with the given constraints → Choosing how to build a face that was transformable

    **Outcome:** Reflection upon which parts of the tool should be visual, and which should be hidden, which prompted reflection about how these choices also are a part of digital visualisations etc.
3. **Technical layer/Arduino:** Transforming dataset into variables that could be processed in a code written in C → Mapping these variables to degrees on servo motors that controlled the position of the face. 
    
    **Outcome:** The process of connecting visual output of a face, with mechanical motors which were controlled by a chosen dataset, showed much difficulty. A goal that seemed simple at first, had many constraints that we needed to work with or around. This resulted in an end result that was not as smooth as expected, creating disturbances for the observer of the tool. Although, this made us engage in reflection about how the vulnerability of a data visualisation can work as an argument to uncover the hidden biases in the work, and visualise not only an end product but also the process of a data visualisation. 

Ratto argues in “The Critical Makers Reader”, that with his approach to critical making the objects that are created is not the main outcome, instead: “[...] the intended results of these experiences are personal, sometimes idiosyncratic transformations in a participants understanding and investment regarding critical/conceptual issues.” (Ratto & Hertz 2019, 24). Starting with the initial idea of making a tool, that can visualise irregularities in datasets, we ended up with a broader conceptual understanding of data visualisations, through the process of critical making. 


#### Main points of discussion:

* What were the most important things we learned through the process? 
* What is the purpose of our tool - how can we compare it to the good trick, is it doing the work of feminist data visualisation? 
* How does the experience of this visceral type of data visualization differ from a more common style?


#### Bibliography: 

Chernoff, H., The Use of Faces to Represent Points in k-Dimensional Space Graphically, Journal Of The American Statistical Association, 68(342) (1973), 361-368.

Haraway, Donna. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist studies, 14(3), 575-599.

Kitchin, Rob, and Travey P Lauriault (2014). “Towards critival data studies: Charting and unpacking data assemblages and their work”. 

Ratto, Matt & Hertz, Garnet, “Critical Making and interdisciplinary learning: Making as a Bridge between Art, Science, Engineering and Social Interventions” In Bogers, Loes, and Letizia Chiappini, eds. The Critical Makers Reader: (Un)Learning Technology. the institute of Network Cultures, Amsterdam, 2019, pp. 17-28. 


### Process:

[Link for process site](https://hackmd.io/KauNqt24SX62ZDpMPYU12w)
[Brainstorm/ideas for project](https://hackmd.io/a4yVt49iQzGJgvNZaW8hNQ)


